Agent Evaluation Readiness Checklist

By Victor Moreira, Deployed Engineer @ LangChain

This checklist is a practical companion to "Agent Observability Powers Agent Evaluation", which covers why agent evaluation is different from traditional software testing, introduces the core observability primitives (runs, traces, threads), and explains how they map to evaluation levels. Read that post first if you're new to agent evaluation.

This post focuses on the ** how,** a step-by-step checklist for building, running, and shipping agent evals.

Start with the simplest eval that gives you signal. A few end-to-end evals that test whether your agent completes its core tasks will give you a baseline immediately, even if your architecture is still changing. Only add complexity when you have evidence that simpler approaches are missing real failures.

Before you build evals

☑️ Manually review 20-50 real agent traces before building any eval infrastructure

☑️ Define unambiguous success criteria for a single task

☑️ Separate capability evals from regression evals

☑️ Ensure you can identify and articulate why each failure occurs

☑️ Assign eval ownership to a single domain expert

☑️ Rule out infrastructure and data pipeline issues before blaming the agent

Deep dive

Manually review 20-50 real agent traces before building any eval infrastructure

Use LangSmith to go from traces to the annotation queue to datasets & experiments.

Before building any infrastructure, spend 30 minutes reading through real agent traces. You'll learn more about failure patterns from this than from any automated system. LangSmith's traces and annotation queues are excellent for this.

Define unambiguous success criteria for a single task

If two experts can't agree on pass/fail, the task needs refinement:

Unclear success:"Summarize this document well." Clear success:"Extract the 3 main action items from this meeting transcript. Each should be < 20 words and include an owner if mentioned."

Separate capability evals from regression evals

You need both because they serve different purposes. Capability evals push your agent forward by measuring progress on hard tasks, while regression evals protect what already works. Without the separation, you'll either stop improving because you're only guarding existing behavior, or you'll ship regressions because you're only chasing new capabilities.

Capability evals answer "what can it do?" - Start with a low pass rate and give you a hill to climb.

Regression evals answer "does it still work?" - Should have ~100% pass rate and catch backsliding.

Ensure you can identify and articulate why each failure occurs

If you can't articulate why something failed, you need more error analysis before building automated evals. This is where you should spend 60-80% of your eval effort. Follow this process:

1. Gather traces: Collect representative failures from production or testing
2. Open coding: Review traces with a domain expert, noting every issue you see without pre-categorizing
3. Categorize: Group issues into a failure taxonomy (prompt problems, tool design problems, model limitations, tool failures, data gaps, etc.)
4. Iterate: Keep reviewing until you stop discovering new failure categories

Assign eval ownership to a single domain expert

Someone needs to own the eval process: maintaining datasets, recalibrating judges, triaging new failure modes, and deciding what "good enough" means. Ideally one domain expert acts as the quality arbiter for ambiguous cases rather than designing by committee.

Rule out infrastructure and data pipeline issues before blaming the agent

The Witan Labs team found that a single extraction bug moved their benchmark from 50% to 73%. Infrastructure issues (timeouts, malformed API responses, stale caches) frequently masquerade as reasoning failures. Check the data pipeline first.

Choose your evaluation level

Not all evals test the same thing. Match your evaluation to the right level of agent behavior.

Single-step vs. Full-turn vs. Multi-turn evals

• Single-step evals: These answer: "Did the agent choose the right tool?" "Did it generate a valid API call?"
• Full-turn evals: Grade a full trace across three dimensions: Final response, Trajectory, and State changes.
• Multi-turn evals: The hardest level to implement. Use N-1 testing: Take real conversation prefixes from production and let the agent generate only the final turn.

Dataset construction

• Ensure every task is unambiguous, with a reference solution that proves it's solvable.
• Test both positive cases (behavior should occur) and negative cases (behavior should not occur).
• Set up a trace-to-dataset flywheel for continuous improvement.